Add to ORKGAbstract. This paper explores statistical properties of a particular Exponential Random Graph Model, the two star probability distribution on the space of simple graphs. Non degenerate lim-iting distributions for the number of edges is derived for all parameter domains, and is shown to have similar phase transition properties as the magnetization in the Curie-Weiss model of statis-tical physics. As a consequence estimates for both parameters are derived, which are consistent irrespective of the phase transition. 1
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
Abstract. We define a general class of network formation models, Statistical Expo-nential Random Gra...
Abstract. This paper gives a way to simulate from the two star probability distribution on the space...
Abstract. We introduce a new method for estimating the parameters of exponential random graph models...
Abstract. The ‘classical ’ random graph models, in particular G(n, p), are ‘homogeneous’, in the sen...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
We consider the edge-triangle model, a two-parameter family of exponential random graphs in which de...
The classical random graph models, in particular G(n,p), are homogeneous, in the sense that the ...
Abstract. We study the asymptotics for sparse exponential random graph models where the parameters m...
We consider the edge-triangle model, a two-parameter family of exponential random graphs in which de...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
Abstract. We define a general class of network formation models, Statistical Expo-nential Random Gra...
Abstract. This paper gives a way to simulate from the two star probability distribution on the space...
Abstract. We introduce a new method for estimating the parameters of exponential random graph models...
Abstract. The ‘classical ’ random graph models, in particular G(n, p), are ‘homogeneous’, in the sen...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
We consider the edge-triangle model, a two-parameter family of exponential random graphs in which de...
The classical random graph models, in particular G(n,p), are homogeneous, in the sense that the ...
Abstract. We study the asymptotics for sparse exponential random graph models where the parameters m...
We consider the edge-triangle model, a two-parameter family of exponential random graphs in which de...
The most promising class of statistical models for expressing structural properties of social networ...
The most promising class of statistical models for expressing structural properties of social networ...
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
The unconstrained exponential family of random graphs assumes no prior knowledge of the graph before...
Abstract. We define a general class of network formation models, Statistical Expo-nential Random Gra...